Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++
By J. M. Roberts, B. Best, D. Dunn, Eric A. Treml, Patrick N. Halpin. Environmental Modelling & Software, 2010
402 citations recorded by the source corpus.
With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and C++, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability between them. In this paper, we describe MGET's software architecture and the tools in the collection. Next, we present an example application: a habitat model for Atlantic spotted dolphin (Stenella frontalis) that predicts dolphin presence using a statistical model fitted with oceanographic predictor variables. We conclude by discussing the lessons we learned engineering a highly integrated tool framework.
Subfields: Ecological Modeling, Ecology, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/53e27257029f0f064bc55b89cff34096a9824f7e
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Citations in the corpus
- Dynamic ocean management: Defining and conceptualizing real-time management of the ocean (2015, neutral): “Some platforms exist, such as SeaSketch and eCatch [46,61], as do the statistical tools to support expansion of these platforms to incorporate more complex models [62], but these applications need addi-”
- Use of predictive habitat modelling to assess the distribution and extent of the current protection of ‘listed’ deep‐sea habitats (2013, methodology): “Presence and absence data points in ARCGIS were overlaid with accompanying environmental variables and the data extracted for use in MAXENT using the Marine Geospatial Ecology Tools add-on (Roberts et al., 2010).”